Using Image Recognition Technology and Smartphones to Improve Trichiasis Surgery Outcomes

摘要 沙眼倒睫(TT)手术后的结果通常是不理想的，术后的发生率 倒睫症(PTT)术后一年内10%~50%不等，中度至重度眼皮 轮廓异常(ECA)发生的几率为10%。尼日利亚是世界上第二大 全球有倒睫病例，目前有10万人需要手术矫正。这些人生活在 在农村社区，获得卫生保健的机会有限。因此，手术是由眼科护士(倒睫)提供的 外科医生“)，他们接受关于如何进行TT手术的简短课程，然后被要求进行手术 在他们所在地区的村庄中设立营地。支持性的监督往往是缺乏的。在这个项目中，我们计划 开发一种mHealth工具，为卫生工作者提供关于手术的即时反馈，并 就是否需要进行额外的手术调整提供指导。 我们之前的工作已经证明，术后立即出现的眼皮是一个强有力的预测因子。 后来手术成功；我们团队中的一位外科医生能够预测70%的PTT病例和85%的眼皮 基于闭合时每个眼皮的单张照片的术后6周的轮廓异常病例 做手术。此外，我们的团队已经证明，我们可以开发图像识别软件来检测眼皮 使用我们正在进行的NEI资助的临床试验的照片，TT的准确率为90%(灵敏度为92%)。在 提议的项目，在R21阶段，我们计划使用同一试验中的图像来开发一台机器 可以预测眼皮是否会出现眼皮轮廓异常或PTT的学习算法 (目标1)。这一算法将被整合到一款智能手机应用程序中，允许外科医生在 手术结束后，立即收到关于眼皮是否可以从调整中受益的反馈 如收紧或松开缝线(目标2)。在R33阶段，我们将测试 使用迭代的、以用户为中心的方法的算法和应用程序(目标3)。然后我们会和尼日利亚人合作 卫生部及其合作伙伴制定和测试APP部署和手术监测(AIM)协议 4)。最后，使用来自Aim 3的数据，我们将使用以下方法评估该应用程序对长期服务交付的影响 经济模型，以估计通过在全球使用该应用程序可以避免的不良结果的数量 (目标5)。该项目有可能极大地改善全球倒立手术的结果。此外， 这个项目可以用来证明远程技术可以用来帮助外科医生和其他 卫生工作者在资源匮乏的环境中工作。

ABSTRACT Outcomes following surgery for trachomatous trichiasis (TT) are often suboptimal, with rates of post-operative trichiasis (PTT) ranging from 10% - >50% within one year following surgery and moderate to severe eyelid contour abnormalities (ECA) occurring >10% of the time. Nigeria is home to the second largest number of trichiasis cases globally, with >100,000 individuals currently needing surgical correction. These individuals live in rural communities, with limited access to health care. Thus, surgery is provided by eye nurses (“trichiasis surgeons”) who receive a brief course on how to perform TT surgery and then are asked to conduct surgical camps in villages across their region. Supportive supervision is often lacking. In this project, we plan to develop an mHealth tool to provide health workers with immediate feedback on their surgery, and to provide guidance on whether additional surgical adjustment is warranted. Our prior work has demonstrated that the immediate post-operative eyelid appearance is a strong predictor of later post-operative success; a surgeon on our team was able to predict 70% of PTT cases and 85% of eyelid contour abnormality cases at six weeks post-op based on a single photograph of each eyelid at the close of surgery. Further, our team has shown that we can develop image recognition software to detect eyelids with TT with >90% accuracy (sensitivity 92%) using photographs from our ongoing NEI-funded clinical trial. In the proposed project, during the R21 phase, we plan to use images from the same trial to develop a machine learning algorithm that can predict whether an eyelid will develop either an eyelid contour abnormality or PTT (AIM 1). This algorithm will be incorporated into a smartphone app that will allow surgeons to take a picture at the end of surgery and receive immediate feedback on whether the eyelid could benefit from an adjustment such as tightening or loosening the sutures (AIM 2). In the R33 phase, we will test the functionality of the algorithm and app using an iterative, user-focused approach (AIM 3). Then we will work with the Nigerian Ministry of Health and partners to develop and test a protocol for app deployment and surgical monitoring (AIM 4). Finally, using data from Aim 3, we will assess the impact of the app on long-term service delivery by using economic models to estimate the number of poor outcomes that could be averted by using the app globally (AIM 5). This project has the potential to dramatically improve trichiasis surgery outcomes worldwide. Further, this project can be used as a proof-of principle that remote technology can be used to aid surgeons and other health workers operating in resource-poor settings.